Want to influence the world? Map reveals the best languages to speak

Many books are translated into and out of languages such as English, German, and Russian, but Arabic has fewer translations relative to its many speakers. (Arrows between circles represent translations; the size of a language’s circle is proportional to the number of people who speak it.)

Speak or write in English, and the world will hear you. Speak or write in Tamil or Portuguese, and you may have a harder time getting your message out. Now, a new method for mapping how information flows around the globe identifies the best languages to spread your ideas far and wide. One hint: If you’re considering a second language, try Spanish instead of Chinese.

The study was spurred by a conversation about an untranslated book, says Shahar Ronen, a Microsoft program manager whose Massachusetts Institute of Technology (MIT) master’s thesis formed the basis of the new work. A bilingual Hebrew-English speaker from Israel, he told his MIT adviser, César Hidalgo (himself a Spanish-English speaker), about a book written in Hebrew whose translation into English he wasn’t yet aware of. “I was able to bridge a certain culture gap because I was multilingual,” Ronen says. He began thinking about how to create worldwide maps of how multilingual people transmit information and ideas.

Ronen and co-authors from MIT, Harvard University, Northeastern University, and Aix-Marseille University tackled the problem by describing three global language networks based on bilingual tweeters, book translations, and multilingual Wikipedia edits. The book translation network maps how many books are translated into other languages. For example, the Hebrew book, translated from Hebrew into English and German, would be represented in lines pointing from a node of Hebrew to nodes of English and German. That network is based on 2.2 million translations of printed books published in more than 1000 languages. As in all of the networks, the thickness of the lines represents the number of connections between nodes. For tweets, the researchers used 550 million tweets by 17 million users in 73 languages. In that network, if a user tweets in, say, Hindi as well as in English, the two languages are connected. To build the Wikipedia network, the researchers tracked edits in up to five languages done by editors, carefully excluding bots.

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